Deep Learning for Adrenal Gland Segmentation: Comparing Accuracy and Efficiency Across Three Convolutional Neural Network Models by Vlad-Octavian Bolocan, Oana Nicu-Canareica, Alexandru Mitoi, Maria Glencora Costache, Loredana Sabina Cornelia Manolescu, Cosmin Medar, Viorel Jinga

“…Adrenal glands are vital endocrine organs whose accurate segmentation on CT imaging presents significant challenges due to their small size and variable morphology. This study evaluates the efficacy of deep learning approaches for automatic adrenal gland segmentation from multiphase CT scans. …”
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A Reinforced, Event-Driven, and Attention-Based Convolution Spiking Neural Network for Multivariate Time Series Prediction by Ying Li, Xikang Guan, Wenwei Yue, Yongsheng Huang, Bin Zhang, Peibo Duan

“…Despite spiking neural networks (SNNs) inherently exceling at processing time series due to their rich spatio-temporal information and efficient event-driven computing, the challenge of extracting complex correlations between variables in multivariate time series (MTS) remains to be addressed. …”
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Improving Prediction of Marine Low Clouds Using Cloud Droplet Number Concentration in a Convolutional Neural Network by Yang Cao, Yannian Zhu, Minghuai Wang, Daniel Rosenfeld, Chen Zhou, Jihu Liu, Yuan Liang, Kang‐En Huang, Quan Wang, Heming Bai, Yichuan Wang, Hao Wang, Haipeng Zhang

“…CNNMet‐Nd demonstrates superior performance, explaining over 70% of the variance in these three cloud variables for scenes of 1° × 1°, a notable improvement over past efforts. …”
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Monte Carlo-Based Strategy for Assessing the Impact of EEG Data Uncertainty on Confidence in Convolutional Neural Network Classification by Pierre Sedi Nzakuna, Vincenzo Gallo, Vincenzo Paciello, Aime Lay-Ekuakille, Angelo Kuti Lusala

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An integrated stacked convolutional neural network and the levy flight-based grasshopper optimization algorithm for predicting heart disease by Syed Muhammad Salman Bukhari, Muhammad Hamza Zafar, Syed Kumayl Raza Moosavi, Majad Mansoor, Filippo Sanfilippo

“…Accurate and early prediction of heart disease remains a significant challenge due to the complexity of symptoms and the variability of contributing factors. This study proposes a novel hybrid model integrating a Stacked Convolutional Neural Network (SCNN) with the Levy Flight-based Grasshopper Optimization Algorithm (LFGOA) to address this challenge. …”
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Cross-species AI: shifting a convolutional neural network from pigs to lambs to detect pneumonia at slaughter by Anastasia Romano, Antonio De Camillis, Domenico Sciota, Simona Baghini, Andrea Di Provvido, Alfonso Rosamilia, Andrea Capobianco Dondona, Nicola Bernabò, Francesca Vaccarelli, Attilio Corradi, Giuseppe Marruchella

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Restricted Boltzmann machine with Sobel filter dense adversarial noise secured layer framework for flower species recognition by Shyamala Devi M, Sundaravadivazhagan Balasubaramanian, Balasubramaniam S, Mohd Asif Shah

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A Lightweight Rolling Bearing Fault Diagnosis Method Based on Multiscale Depth-Wise Separable Convolutions and Network Pruning by Qingming Hu, Xinjie Fu, Dandan Sun, Donghui Xu, Yanqi Guan

“…Finally, the generalizability of the model under variable operating conditions is discussed. Compared with other intelligent diagnosis methods, the presented strategy can achieve smaller model size and higher accuracy under both constant and variable working conditions.…”
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Brain age prediction from MRI images based on a convolutional neural network with MRMR feature selection layer by Mustafa Hatem Al Ghariri, Seyed Omid Shahdi

“…The feature selection layer uses MRMR algorithm which identifies essential characteristics for a target variable while minimizing feature redundancy to select the optimal feature subset. …”
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Fault Detection and Diagnosis in Air-Handling Unit (AHU) Using Improved Hybrid 1D Convolutional Neural Network by Prince, Byungun Yoon, Prashant Kumar

“…While conventional convolutional neural networks (CNNs) effectively detect defects, incorporating more spatial variables could enhance their performance further. …”
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Lung and Colon Cancer Classification Using Multiscale Deep Features Integration of Compact Convolutional Neural Networks and Feature Selection by Omneya Attallah

“…Finally, to benefit from multiple deep network architectures while reducing classification complexity, the proposed CAD merges dual deep layer variables of the three CNNs and then applies the analysis of variance (ANOVA) and Chi-Squared for the selection of the most discriminative features from the integrated CNN architectures. …”
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CMDMamba: dual-layer Mamba architecture with dual convolutional feed-forward networks for efficient financial time series forecasting by Zhenkai Qin, Zhenkai Qin, Zhenkai Qin, Baozhong Wei, Baozhong Wei, Yujia Zhai, Ziqian Lin, Xiaochuan Yu, Xiaochuan Yu, Jingxuan Jiang

“…The CMDMamba model employs a dual-layer Mamba structure that effectively captures price fluctuations at both the micro- and macrolevels in financial markets and integrates an innovative Dual Convolutional Feedforward Network (DconvFFN) module. …”
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Long-term prediction of Poyang Lake water level by combining multi-scale isometric convolution network with quantile regression by Ying Jian, Yong Zheng, Gang Li, Siyang Yao, Tianfu Wen, Zhangjun Liu

“…This study offers an effective solution for the prediction of PYL WL with highly complex characteristics, which can be also extended to other hydrological variables.…”
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Meta-learning based softmax average of convolutional neural networks using multi-layer perceptron for brain tumour classification by Irwan Budi Santoso, Shoffin Nahwa Utama, Supriyono

“…Convolutional Neural Networks (CNNs) are commonly used due to their proven performance, but their effectiveness diminishes with the high variability of tumour characteristics. …”
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Perbandingan Metode Random Forest, Convolutional Neural Network, dan Support Vector Machine Untuk Klasifikasi Jenis Mangga by Ricky Mardianto, Stefanie Quinevera, Siti Rochimah

“… Mango is a fruit known as the "King of Fruit" due to its rich flavor, vast variability, and high nutritional value. Classifying mangoes based on their external appearance is the initial step in the process of identifying and categorizing mango types conventionally. …”
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Acceleration of Urdu Optical Character Recognition on Zynq UltraScale+ MPSoC Using Deep Convolutional Neural Network by Fauzia Yasir, Majida Kazmi

“…Existing FPGA-based OCR implementations have primarily focused on simplified datasets such as MNIST digits, limiting their generalizability to scripts like Urdu that exhibit extensive intra-class variability, contextual shaping, and diacritics. This study presents a hardware-accelerated Urdu OCR framework using a custom-designed Convolutional Neural Network (CNN) optimized for deployment on the Xilinx Zynq UltraScale+ MPSoC (ZCU104). …”
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Research on foreign object intrusion detection in railway tracks based on MSL-YOLO by Hongxia Niu, Dingchao Feng, Tao Hou

“…Abstract Railway foreign object intrusion detection poses significant challenges due to complex backgrounds, variable lighting conditions, and the need for real-time, multi-scale object detection. …”
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Prediction of Atmospheric Refractivity From Clutter Power Images Using a Convolutional Neural Network and a Trilinear Atmospheric Model by Taekyeong Jin, Doyoung Jang, Hosung Choo

“…These input data, generated by the Advanced Refractive Prediction System (AREPS) EM simulator, using four variables of a theoretical trilinear atmospheric model. …”
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Exploring the Latent Information in Spatial Transcriptomics Data via Multi‐View Graph Convolutional Network Based on Implicit Contrastive Learning by Sheng Ren, Xingyu Liao, Farong Liu, Jie Li, Xin Gao, Bin Yu

“…Experimental data confirms that STMIGCL significantly enhances spatial domain recognition precision and outperforms all baseline methods in tasks such as trajectory inference and Spatially Variable Genes (SVGs) recognition.…”
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Vibration-based gearbox fault diagnosis using a multi-scale convolutional neural network with depth-wise feature concatenation. by Van-Trang Nguyen, Quoc Bao Diep

“…This article proposes a novel approach for vibration-based gearbox fault diagnosis using a multi-scale convolutional neural network with depth-wise feature concatenation named MixNet. …”
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